A MULTIFACETED ANALYSIS OF PIONEERING STRATEGIES AND LEADING-EDGE TECHNOLOGY FOR WASTE SEGREGATION

Abstract

This comprehensive review explores a multitude of waste segregation techniques and technologies employed in waste management. It covers various methodologies, including Deep Learning, Hyperspectral Imaging, Robotics, Optical Sensors, and more, each designed to improve waste sorting and enhance recycling efforts. The study provides a detailed comparison of these technologies, highlighting their accuracy rates and the specific types of waste they are designed to handle. These technologies offer promising solutions to address the growing challenge of waste management and environmental sustainability. The findings presented in this review serve as a valuable resource for researchers, policymakers, and waste management professionals working towards more efficient and sustainable waste segregation and recycling practices.

Keywords: Waste segregation, Digital Image processing, Solid Waste, CNN, Raspberry Pi, SVM.